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\title{Revisiting Optimal Decoding for Machine Translation IBM Model~4}


\author{
Sebastian Riedel\footnotemark[1] \footnotemark[2]    \qquad James Clarke\footnotemark[3]  \\
\footnotemark[1]  {Department of Computer Science, University of Tokyo, Japan}\\
\footnotemark[2]  {Database Center for Life Science, Research
Organization of Information and System, Japan}\\
\footnotemark[3]  {Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801}\\
\
\footnotemark[1]  \texttt{sebastian.riedel@gmail.com} \qquad \footnotemark[2] \texttt{clarkeje@gmail.com}
}

\date{}

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\begin{abstract}
  This paper revisits optimal decoding for statistical machine
  translation using IBM Model~4.  We show that exact/optimal inference
  using Integer Linear Programming is more practical than previously
  suggested when used in conjunction with the Cutting-Plane Algorithm.
  In our experiments we see that exact inference can provide a gain of
  up to one BLEU point for sentences of length up to 30 tokens.
\end{abstract}

\section{Introduction}
\label{sec:introduction}
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\section{IBM Model~4} 
\label{sec:ibm-model-4}
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\section{Integer Linear Programming Formulation}
\label{sec:ilp}
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\section{Cutting Plane Algorithm}
\label{sec:cutting-plane}
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\section{Evaluation}
\label{sec:evaluation}
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\section{Discussion and Conclusions}
\label{sec:conclusion}
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\section*{Acknowledgements}
We would like to thank Ulrich Germann and Daniel Marcu for their help
with the ISI ReWrite Decoder.


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